5 results about "Spatial data mining" patented technology

The application discloses a kind of based on spatial data mining and geological constraint seismic activity fault model construction method, system, equipment and medium.The method is first arranged and analyzed to seismic data, extracts minimum complete subdirectory;Using improved mean shift algorithm for spatial clustering, identify each active fault corresponding small earthquake cluster;Subsequently, each fault cluster is three-dimensional slice, and least square method fitting is generated fault interpretation line;With interpretation line as foundation, initial seismic activity fault three-dimensional model is constructed, and is optimized by multi-source geological constraint, and active fault three-dimensional fine model is obtained;Finally, the model is integrated with digital earth spatial registration.This application can realize the whole process automation and quantization from seismic data analysis to active fault three-dimensional fine model construction and application, improve the objectivity, efficiency, precision and reliability of model construction, provide important technical support for seismic disaster risk assessment, active fault detection.

This invention relates to the field of engineering geological exploration technology, and in particular to a method for identifying geological factors of ground subsidence risk. It utilizes nested spatial data mining to obtain the spatial correlation between ground subsidence and geological information, and identifies the ranking of geological information indicators affecting ground subsidence. A natural discontinuity grading method is used to classify the level of ground subsidence risk based on the ranking of geological information indicators. A Bayesian algorithm is employed to divide the range of values ​​for geological factor indicators at different ground subsidence risk levels. The advantages of this invention are: (1) It uses measured data to mine risk factors and their level ranges, accurately assessing ground subsidence risk; (2) It does not determine risk levels and indicator ranges based on expert knowledge and prior assumptions, thus avoiding the decrease in accuracy caused by human judgment and differences between experience and actual engineering; (3) This invention advances risk factor identification from qualitative classification to quantitative identification.

The application discloses a hierarchical spatial community mining method based on Gosper-island and belongs to the field of spatial data mining. First, the application carries out hierarchical partitioning of space by adopting Gosper-island, uses hierarchical partitioning coding to record the spatial hierarchical constraint of a partitioning unit, and uses Gosper space filling coding to record the spatial proximity constraint of the same hierarchical partitioning unit. Then, the hierarchical spatial network is constructed by taking the partitioning unit as a network node, the network is organized in the form of a network adjacency matrix, the network adjacency matrix heat map is drawn according to the Gosper coding sequence of the node, and finally, the hierarchical matrix block structure of the network heat map at different levels is extracted from top to bottom to obtain the hierarchical spatial community structure meeting the spatial hierarchical constraint. The application conforms to the hierarchical characteristics of the hierarchical spatial community and can effectively avoid the problems caused by the partitioning hierarchical tree.

The invention discloses a low-efficiency monitoring method, device and equipment for a fan unit and a readable storage medium, and belongs to the technical field of wind power generation. The method comprises the steps that measuring point data of the fan unit is acquired; historical wind speed distribution characteristics of the fan units are extracted from the measuring point data, and fan unit clusters are dynamically grouped; determining a single-machine low-efficiency deviation index and a cluster low-efficiency deviation index according to the measuring point data; performing low-efficiency state judgment according to the single-machine low-efficiency deviation index and the cluster low-efficiency deviation index; spatio-temporal data mining is carried out on the low-efficiency unit, and low-efficiency root cause mapping is carried out; and the low-efficiency unit is a fan unit in a low-efficiency state. According to the method, day-level automatic diagnosis and historical low-efficiency state mining of the low-efficiency state of the fan unit can be achieved, accurate identification and root cause mapping of the low-efficiency state are achieved, a quantitative basis is provided for fan operation and maintenance decision making, and therefore the operation and maintenance precision and efficiency are improved.

The present application is suitable for the field of geographic information processing and spatial data mining technology, and provides a kind of geographic feature causal mining method considering topological neighborhood, comprising the following steps: obtaining the data of multiple types of geographic features in the target area, and pre-processing, obtaining the geographic feature set containing spatial position coordinates and category attributes;Based on the geographic feature set, the target area is divided into research units, and a plurality of basic research units are obtained, and based on the category attribute of the geographic feature, the spatial distribution of different category geographic features in each basic research unit is counted, and the distribution characteristic value is obtained.The quantitative identification of the causal action direction and the causal action intensity between geographic features can be realized;The structural connection between geographic features can be effectively described, the influence of pseudo correlation is reduced, and the accuracy and stability of causal relationship identification are improved.